{"as_of":"2026-08-18T06:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:56711cbd0a3314977a57570468a438feb008296a02c56ae8b30078e874b1afe6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:47:05.670497Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T04:47:05.918689Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.07940","last_updated":"2021-10-15T08:41:51Z","snapshot_observed_at":"2026-08-17T19:32:32.740331Z","submitted_at":"2021-10-15T08:41:51Z","title":"Wasserstein Unsupervised Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2110.07940","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.07940","snapshot_observed_at":"2026-08-16T04:47:05.918689Z","title":"Wasserstein Unsupervised Reinforcement Learning","venue":"cs.LG","work_id":"c6cb7bd7-5ab4-47a3-b1c0-2a74ca3c8403","year":2021},"citing_paper":{"arxiv_id":"2505.00663","last_updated":"2025-05-01T17:07:01Z","snapshot_observed_at":"2026-08-17T05:45:08.928570Z","submitted_at":"2025-05-01T17:07:01Z","title":"Wasserstein Policy Optimization","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-16T04:47:05.670497Z"},"links":{"cited_paper":"/paper/2110.07940","citing_paper":"/paper/2505.00663"},"observation_digest":"sha256:16a0d22447a57f26f3aa342ada25c079509a7ee37ac1fbe4976ddb34f80a9343","observation_id":"857f1670-9d4e-432b-99e7-5be13481fc37","resolution":{"observed_at":"2026-08-16T04:47:05.922391Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.07940","last_updated":"2021-10-15T08:41:51Z","snapshot_observed_at":"2026-08-17T19:32:32.740331Z","submitted_at":"2021-10-15T08:41:51Z","title":"Wasserstein Unsupervised Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.07940","snapshot_observed_at":"2026-08-04T19:45:32.998178Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01851","last_updated":"2026-08-03T07:58:35Z","snapshot_observed_at":"2026-08-17T12:44:12.584111Z","submitted_at":"2026-08-03T07:58:35Z","title":"Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-04T19:45:32.998178Z"},"links":{"cited_paper":"/paper/2110.07940","citing_paper":"/paper/2608.01851"},"observation_digest":"sha256:dccfe5fef1ef25bc5f00830ca8d5cc24edd6c7a91f379b9ba6e21c8011e8488d","observation_id":"39d1511c-1f63-41a5-933a-69effd70f3a2","resolution":{"observed_at":"2026-08-04T19:45:32.998178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2110.07940/citation-record","integrity":"/paper/2110.07940/integrity","json":"/paper/2110.07940/citation-record.json","paper":"/paper/2110.07940"},"outbound":[],"paper":{"arxiv_id":"2110.07940","last_updated":"2021-10-15T08:41:51Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T19:32:32.740331Z","submitted_at":"2021-10-15T08:41:51Z","title":"Wasserstein Unsupervised Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.07940."}